Abstract B72: Assessing specific biological mechanisms of drug resistance in adult and pediatric AML.
Notice bibliographique
Résumé
Abstract Background: Acute myeloid leukemias (AML) non-responsive to induction chemotherapy generally have poor prognoses. Understanding the biological mechanisms of drug resistance, or sensitivity, specific to each individual AML could inform biologically-based treatment selection in the salvage setting. The current study was undertaken to identify resistance mechanisms to commonly used AML drugs. Methods: Single Cell Network Profiling (SCNP) is a multiparametric flow cytometry-based assay that simultaneously measures, in a quantitative fashion and at the single cell level, both extracellular surface marker levels and changes in intracellular signaling proteins in response to extracellular modulators (Kornblau et al. Clin Cancer Res 2010). Herein we use SCNP to investigate biological mechanisms of in vitro resistance to chemotherapeutics relevant to AML treatment (Ara-C/Daunorubicin, Gemtuzumab Ozogamicin, Decitabine, Azacitidine and Clofarabine) by assessing in a quantitative fashion the induction of DNA Damage Responses (as measured by phospho-Chk2 and phospho-H2A.X levels) and apoptosis (as measured by cleaved PARP levels and loss of membrane integrity) in 6 pediatric and 6 elderly AML samples. In addition, SCF and FLT3L induced survival signaling (as measured by phospho-Akt and phospho-Erk levels) were simultaneously assessed. Results: Treatment with Ara-C/Dauno and GO revealed cross-resistance among AML samples and was associated with elevated SCF induced PI3K/Erk survival signaling. In vitro treatment of AML samples with the different genotoxic agents (Ara-C/Dauno, GO or CLO), resulted in three distinct profiles of DNA Damage and apoptosis responses: A) Samples with induced DNA Damage Response and apoptosis, B) Samples with induced DNA Damage response but no detectable apoptosis and C) Samples lacking significant induced DNA Damage response and apoptosis. In the latter group the presence of drug efflux activity in some samples, as measured by sensitization with an MDR1 (MultiDrug Resistance) inhibitor, may explain the observation. Of note, for each sample the drug response varied with the specific agent tested. Differential sensitivity among individual patient samples was also observed between treatment with two hypomethylating agents (Decitabine and Azacitidine). Additionally, distinct kinetics of DNA Damage and apoptosis were observed among drugs: genotoxic agents (CLO, Ara/Dauno, GO) induced DNA Damage prior to induction of apoptosis while hypomethylating agents (Decitabine and Azaciditine) induced DNA Damage coincident with induction of apoptosis. Conclusions: Using SCNP to dissect in vitro response to clinically used AML drugs reveals: 1) heterogeneity of individual patient biology characteristic of AML, 2) distinct resistance mechanisms present in unique samples and 3) mechanistic differences between distinct classes of agents. Studies are ongoing to assess the clinical relevance of these findings. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2011 Nov 12-16; San Francisco, CA. Philadelphia (PA): AACR; Mol Cancer Ther 2011;10(11 Suppl):Abstract nr B72.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».